If you’re on your way to hiring your data analyst, what comes next is establishing the foundation that will set them up for success at your company. Your job isn’t done when the candidate accepts your offer.
Onboard your new data analyst with a 30-60-90 day plan for success
Think of your first data analyst hire as the person in charge of developing your data into a single, accurate source of truth. Data will now be owned by someone. They’ll be developing data that’s sourced from all teams — engineering, marketing, sales, product, etc. — and uncovering opportunities that aren’t visible to individual teams.
You poured a great deal of time into finding, interviewing, and hiring your new data analyst — protect your investment and be sure you have a plan for their first 90 days before onboarding even starts.
Get specific, but stay flexible
The best 30-60-90-day success plan will allow for input from your new data analyst, but it’ll be easier on them if you provide expectations and goals on day one. After onboarding and initial meetings with teams, your new data analyst will be able to articulate ideas and then help you refine the plan.
Don’t be afraid to adapt as you go and lead your new hire to success, but start with a solid plan.
Include specific goals
You’re going to want to include some big picture goals like “make data actionable” in your 30-60-90 plan, but that’s principle, and not specific enough for action. Remember, you know more about the current state of data at your company, and the goals of different teams, than your new analyst does.
That’s why specific goals matter. Without them, your analyst might do a lot of good work in their first 90 days, but it won’t necessarily be the right work.
One way to fill out your plan is to check in with other stakeholders about what they would like to see accomplished. A wishlist from every team can help you identify the steps on the path toward achieving bigger picture goals. For example:
Marketing may ask for help assessing multi-touch attribution
Sales could ask for insight on effective cadences for different personas
Product might need assistance analyzing user adoption for new products or features
The specific needs of your company will be unique, and it’s difficult to predict all of them without making sure other stakeholders are committed to the success of data projects. Getting their input is an effective way to align everyone’s expectations.
Don’t overlook the business aspect — and nurturing skills
Regardless of how perfectly qualified your new hire is, they’re still going to have a lot to learn about your organization. Make sure they understand the “why?” behind their early tasks, so they can learn more about the needs of your business. Remember, you’re asking this person to take a quantitative approach to answering a lot of important questions about your business. It’s going to be harder for them to help your business grow if they approach questions from the wrong perspective.
Transforming large data sets in particular is challenging without a practical understanding of the problem. Consider marketing at a company with an e-commerce store. The marketing team might want help analyzing email campaigns. Someone may ask for “the open rate across all our campaigns,” and that isn’t an inherently difficult task — it can typically be found in whatever platform you use to send emails. But if your analyst understands the “why?”, they can uncover more useful insights. For example, new subscribers probably behave differently than loyal customers, promotions around holidays might skew results, etc. Context helps everyone find the answers that’ll move the business forward.
As your new hire learns more about your organization, you have a great opportunity to nurture their skillset. Don’t neglect the soft skills in this process. Hard skills, like writing transforms, will go a long way in helping your new hire succeed. But they also need to understand adaptive problem solving and communication. Besides understanding what other team members are actually asking for, it’s important for your analyst to communicate their methodology and findings in a way that anyone can understand.
You need goals, but your 30-60-90 should not be a to-do list
Don’t weaponize the plan, or treat it like an agenda. There are two main reasons for this.
First, a to-do list removes the element of flexibility described above. Your analyst is more likely to be successful — and an immediate asset to your organization — if your plan can adapt to new priorities while they get up to speed.
Second, an inflexible to-do list means your analyst will be forced to prioritize doing the work that’s listed, not the work that makes the most sense. If one of your teams realizes their initial ask isn’t going to achieve the goal that prompted it, the plan should be adapted.
What you should include in the first draft of the plan will depend on your company’s needs and goals. The real work is in aligning everyone’s expectations about the purpose of the plan itself.
Hit the ground running and monitor progress
All that’s left is to actually put the plan in place with your new analyst! The most important thing to discuss with them is what you really need from your data. If they understand the purpose behind their goals, and you’re there for support, they’re in a position to succeed.
Your data analyst will source, combine, and compare data from different sources to understand what will make a difference for your business. They’ll gather data from multiple departments or parts of the business into a central home, and then clean and democratize the data so it’s usable by decision makers.
Keep an eye on the process and schedule casual check-ins and longer one-on-ones. Even if you’re not a data subject matter expert, you can connect them with resources and mentors, remove obstacles, and provide clarity and alignment on business goals and how the business works. Before long, you should see the benefits of bringing a data analyst onboard.
For more tips on bringing in the first data analyst to your company, watch our panel discussion on when and how to find the right data analyst for your team. You’ll hear from leaders at Google Cloud, thredUP, and Rackhouse VC on how they evaluate candidates and what they’ve learned from years of hiring for data roles.